Jointly Optimizing Data Discretization and Naive Bayes Classifier via Multi-Objective Optimization

Abstract

Data discretization plays a critical role in enhancing the performance of the naive Bayes classifier. Traditional data discretization methods often utilize a two-stage framework, where data discretization and classification are optimized separately, leading to sub-optimal performance. To tackle the issue, we propose a novel multi-objective optimization framework that incorporates the optimization of the naive Bayes classifier into the objective function of optimizing data discretization. To solve this problem, we employ an alternative optimization method to jointly optimize both data discretization and classification. Additionally, to further enhance the optimization process, we leverage a genetic algorithm to explore and exploit a larger solution space. Experimental results on 20 datasets demonstrate that our method outperforms state-of-the-art methods.